The handling of complex objects such as images is becoming a more demanding issue given the ease in which images and other similar media can be created, communicated, and stored. In particular, finding images that look visually similar is becoming a relevant problem. Different analysis techniques exist that have been applied to address this problem. Most of the techniques identify different factors of an image and compare those factors to define a measure of similarity for the images.
For example, a predominant color (e.g., blue) in a first image can be compared to the predominant color of a second image. If there is computed to be a high degree of color similarity it can be determined the images are similar. Additionally or alternatively, a different set of factors can be utilized and compared to determine the degree of similarity, and then to classify images.
A Kohonen neural network, also known as self-organizing map (SOM) is a way of classifying information. In a classic application of a Kohonen neural network to compare images, an uninitialized map (or random map) is used as a starting point. An image is used as an input, and the image trains the map. Eventually, the map vectors align with the content of the input, thereby organizing the information contained in the image. By activating specific vectors in the map, information can be obtained, and the images can be compared. A problem arises when the color content is very broad. Comparing a large set of images becomes difficult, because an equally large map is required.